A Feature Selection Newton Method for Support Vector Machine Classification

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Abstract

A fast Newton method, that suppresses input space features, is proposed for a linear programming formulation of support vector machine classifiers. The proposed stand-alone method can handle classification problems in very high dimensional spaces, such as 28, 032 dimensions, and generates a classifier that depends on very few input features, such as 7 out of the original 28, 032. The method can also handle problems with a large number of data points and requires no specialized linear programming packages but merely a linear equation solver. For nonlinear kernel classifiers, the method utilizes a minimal number of kernel functions in the classifier that it generates.

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